Isolation and Quantification of Uremic Toxin Precursor-Generating Gut

International Journal of Molecular Sciences

Article Isolation and Quantiﬁcation of Uremic Toxin Precursor-Generating Gut Bacteria in Chronic Kidney Disease Patients

Tessa Gryp 1,2,3,* , Geert R.B. Huys 3 , Marie Joossens 3 , Wim Van Biesen 1, 1, 2, Griet Glorieux † and Mario Vaneechoutte † 1 Department of Internal Medicine and Pediatrics, Nephrology Section, Ghent University Hospital, 9000 Ghent, Belgium; [email protected] (W.V.B.); [email protected] (G.G.) 2 Department of Diagnostic Sciences, Laboratory Bacteriology Research, Ghent University, 9000 Ghent, Belgium; [email protected] 3 Department of Microbiology, Immunology and Transplantation, Molecular Microbiology—Microbiome Research Lab, KU Leuven, 3000 Leuven, Belgium; [email protected] (G.R.B.H.); [email protected] (M.J.) * Correspondence: [email protected]; Tel.: +32-9-332-33-64 Equally contributing seniors. † Received: 26 February 2020; Accepted: 11 March 2020; Published: 14 March 2020

Abstract: In chronic kidney disease (CKD), impaired kidney function results in accumulation of uremic toxins, which exert deleterious biological effects and contribute to inflammation and cardiovascular morbidity and mortality. Protein-bound uremic toxins (PBUTs), such as p-cresyl sulfate, indoxyl sulfate and indole-3-acetic acid, originate from phenolic and indolic compounds, which are end products of gut bacterial metabolization of aromatic amino acids (AAA). This study investigates gut microbial composition at different CKD stages by isolating, identifying and quantifying PBUT precursor-generating bacteria. Fecal DNA extracts from 14 controls and 138 CKD patients were used to quantify total bacterial number and 11 bacterial taxa with qPCR. Moreover, isolated bacteria from CKD 1 and CKD 5 fecal samples were cultured in broth medium supplemented with AAA under aerobic and anaerobic conditions, and classified as PBUT precursor-generators based on their generation capacity of phenolic and indolic compounds, measured with U(H)PLC. In total, 148 different fecal bacterial species were isolated, of which 92 were PBUT precursor-generators. These bacterial species can be a potential target for reducing PBUT plasma levels in CKD. qPCR indicated lower abundance of short chain fatty acid-generating bacteria, Bifidobacterium spp. and Streptococcus spp., and higher Enterobacteriaceae and E. coli with impaired kidney function, confirming an altered gut microbial composition in CKD.

Keywords: qPCR; bacterial culture; fecal bacteria; chronic kidney disease; protein-bound uremic toxins; bacterial metabolization; aromatic amino acids; p-cresol; indole; indole-3-acetic acid; phenol

1. Introduction In chronic kidney disease (CKD), impaired kidney function results in the accumulation of uremic toxins [1–3], which exert deleterious biological eﬀects and contribute to inﬂammation and cardiovascular morbidity and mortality [4–8]. Well-known protein-bound uremic toxins (PBUTs), p-cresyl sulfate (pCS), p-cresyl glucuronide (pCG), indoxyl sulfate (IxS) and indole-3-acetic acid (IAA) originate in the colon, where aromatic amino acids (AAA) are metabolized by gut bacteria into phenolic (p-cresol and phenol) and indolic compounds (indole and IAA) [9–12]. p-Cresol and indole are sulfated in the colon mucosa and by the liver into pCS and IS, and a small part is glucuronidated into pCG [9,13,14],

Int. J. Mol. Sci. 2020, 21, 1986; doi:10.3390/ijms21061986 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2020, 21, 1986 2 of 19 while IAA enters the circulation without further modifications [15–17]. These PBUTs reversibly bind to plasma albumin [18]; however, only the free fraction can be removed by dialysis [19,20]. During recent years, there is increasing evidence that the gut bacterial composition is altered in patients with CKD [21–30]. Generally, in CKD, increased growth of both aerobes and anaerobes takes place in the small intestine [24,25], while in the colon the numbers of aerobic bacteria (e.g., Enterobacteriaceae) are increased [23,30] and anaerobic bacteria (e.g., bifidobacteria) are decreased [23,29,30]. The increased aerobic bacterial growth is a consequence of an elevated pH resulting from the high ammonia levels in the gut in CKD [31]. Ammonia is a bacteria-derived hydrolysis end product of urea, which accumulates in the circulation and enters the gut lumen via the entero-hepatic cycle [31] and by diffusion through the disrupted intestinal epithelial barrier as is typically seen in patients with CKD [32,33]. This leads to a uremic milieu in the gut lumen, altering the gut microbial composition in favor of urease expressing bacteria in end stage kidney disease (ESKD) patients [27]. However, in a recent study by our group [34], the generation of fecal PBUT precursor metabolites by fecal bacteria did not alter with CKD progression nor compared to controls. This suggests that the accumulation of PBUTs in plasma is mainly due to the impaired kidney function itself. Nevertheless, minimizing the generation by gut bacteria of phenolic and indolic compounds in the hope to subsequently reduce plasma PBUT levels remains an important goal due to the absence of other adequate therapies to reduce the plasma levels of PBUTs in patients with CKD. It is known that certain bacterial isolates are capable to generate PBUT precursors in an in vitro setting [9,35–40], but the generation capacity of fecal bacteria isolated from patients with CKD has not yet been investigated. Numerous studies in patients with CKD already addressed the idea of applying pro-, pre- and synbiotics [30,41–46] to increase saccharolytic and decrease proteolytic bacteria. Thus far, only one specific PBUT has been targeted [47]. In addition, it has been shown that dietary intake is the most important determinant of the fecal metabolome in CKD, next to the loss of kidney function [13], suggesting that diet could also play an important role in altering the gut bacterial composition in a favorable way. Intake of low-protein diets lowers the serum levels of IxS and the generation of various phenolic and indolic metabolites in healthy humans and mice [48]. In this study, bacterial isolates obtained from fecal samples from patients with CKD stage 1 and stage 5 were taxonomically characterized and metabolically classified as PBUT precursor-generating bacteria based on their capacity to assimilate AAA into phenolic (p-cresol and phenol) and/or indolic (indole and IAA) compounds. In addition, the abundance of specific fecal PBUT precursor-generating bacteria in the different stages of CKD and in controls was investigated.

2. Results

2.1. Culture Media to Isolate Protein-Bound Uremic Toxin Precursor-Generating Bacteria from Fecal Samples To isolate PBUT precursor-generating bacteria, an appropriate culture medium had to be selected. Therefore, seven different media were compared based on the cultivation of a bacterial test panel consisting of 12 known uremic toxin precursor-generating bacterial species (Figure S1), and the cultivation of fecal bacteria from whole fecal samples (Table S1). Besides the commercially available Schaedler medium (SCH, Becton Dickinson, Erembodegem, Belgium), the yeast casitone fatty acid glucose (YCFAG) medium [37,49] was used as the basis for another six media, of which specific components were omitted separately, to check their importance for the cultivation of UT precursor-generating bacteria. Moreover, the effect of the supplementation of AAA and of a fecal suspension from healthy volunteers (FS) was investigated. All bacterial species from the test panel were culturable with the 24 YCFAG media combinations, except for Bifidobacterium sp. and Lactobacillus sp., which only grew when AAA and FS were added to the complete YCFAG (YCFAG ∆0), YCFAG without vitamins (YCFAG ∆V) or YCFAG without short chain fatty acids (YCFAG ∆S). For the SCH medium, addition of AAA was detrimental for the growth of most tested bacterial species, except B. longum, E. coli, L. paracasei, and S. epidermidis. SCH medium, Int. J. Mol. Sci. 2020, 21, 1986 3 of 19 with and without the supplementation of FS, resulted in the growth of all species of the test panel (Figure S1). Based on these results, the five medium formulations that supported growth of all bacterial species (i.e., YCFAG ∆0 +AAA +FS, YCFAG ∆V +AAA +FS, YCFAG ∆S +AAA +FS, SCH and SCH + FS) were used further to culture bacteria from fecal samples. Table S1 provides an overview of all microorganisms that could be cultured on these five media. Most anaerobic and aerobic bacteria were isolated from YCFAG ∆0 +AAA +FS and from SCH, and these media were further used to isolate PBUT precursor-generating bacteria from fecal samples of patients with CKD.

2.2. Isolation and Identification of Protein-Bound Uremic Toxin-Generating Bacteria from Fecal CKD Samples Figure S2 illustrates an overview of the isolation of PBUT precursor-generating bacteria. In total, 148 different bacterial species were isolated from CKD 1 (n = 6) and CKD 5 (n = 6) fecal samples to cover fecal bacteria representative for both early and late stage of CKD. Of these, 99 species could be identified by means of MALDI-TOF (Bruker, Mannheim, Germany) and 92 were considered as PBUT precursor-generating bacteria when, for at least one of the precursor metabolites (i.e., p-cresol, phenol, indole or IAA), the levels were above the limit of detection (LOD) as assessed by U(H)PLC. Table1 lists all isolated bacterial species with their generation capacity of PBUT precursors under aerobic and anaerobic conditions. Generally observed, p-cresol and phenol were mainly generated under anaerobic conditions, while indole and IAA could be generated under both aerobic and anaerobic conditions. Phenolic compounds and IAA were predominantly generated by bacterial species belonging to the Actinomycetaceae, Bacteroidaceae, Clostridiaceae, Enterococcaceae, Lachnospiraceae, Staphylococcaceae and Tannerellaceae, while indolic compounds were mainly generated by the Bifidobacteriaceae, Coriobacteriaceae, Enterobacteriaceae, Propionibacteriaceae and Rikenellaceae. In addition, Bacteroides uniformis, Odoribacter splanchnicus and Oscillibacter sp. are the only bacterial species capable of generating all four PBUT precursor metabolites under anaerobic conditions.

Table 1. Protein-bound uremic toxin precursor-generating capacity of bacteria isolated from fecal samples of patients with chronic kidney disease (CKD).

CKD 1 (n = 6) CKD 5 (n = 6) Bacterial Species Neg pC Phe Ind IAA Neg pC Phe Ind IAA ACTINOBACTERIA Actinomycetaceae Actinomyces sp. O4 O4 Bifidobacteriaceae Bifidobacterium adolescentis N9 N1 Bifidobacterium bifidum c N1 N2 Bifidobacterium catenulatum/ N9 N2 pseudocatenulatum a Bifidobacterium dentium N1 N1 Bifidobacterium longum c N6 N5 Parascardovia denticolens NC Scardovia wiggsiae N1 Brevibacteriaceae Brevibacterium ravenspurgense O1 Coriobacteriaceae Collinsella aerofaciens O1 N4 Corynebacteriaceae Corynebacterium amycolatum O1 O1 Corynebacterium aurimucosum NC NC Corynebacterium coyleae O3 Eggerthellaceae Eggerthella lenta N3 Microbacteriaceae Microbacterium sp. O2 Micrococcaceae Kocuria sp. O1 Pseudoglutamicibacter albus O1 Propionibacteriaceae Cutibacterium acnes N2 N2 N1 N1 Propionibacterium freudenreichii N1 Int. J. Mol. Sci. 2020, 21, 1986 4 of 19

Table 1. Cont.

CKD 1 (n = 6) CKD 5 (n = 6) Bacterial Species Neg pC Phe Ind IAA Neg pC Phe Ind IAA BACTEROIDETES Bacteroidaceae Bacteroides sp. N1 N1 N1 N1 N1 Bacteroides caccae N1 N1 N1 N1 N1 N1 Bacteroides cellulosilyticus N1 N1 Bacteroides coprocola N1 N1 Bacteroides eggerthii N1 N1 N1 N1 N1 N1 Bacteroides faecis N1 N1 Bacteroides ﬁnegoldii N1 N1 N1 Bacteroides fragilis N1 N1 N1 O1 O1 O1 Bacteroides intestinalis NC Bacteroides massiliensis N1 N1 Bacteroides ovatus N1 N1 N1 N1 N1 Bacteroides pyogenes N1 N1 Bacteroides salyersiae N1 N1 Bacteroides stercoris NC Bacteroides thetaiotaomicron N2 N2 Bacteroides uniformis N1 N1 N1 N1 N2 N2 N4 N3 Bacteroides vulgatus N2 N1 N2 N1 N1 Odoribacteriaceae Odoribacter splanchnicus N3 N3 N3 N3 Prevotellaceae Prevotella copri N1 N1 Rikenellaceae Alistipes putredinis N1 N1 Alistipes shahii N2 N2 Tannerellaceae Parabacteroides distasonis N2 N2 Parabacteroides goldsteinii N1 N1 Parabacteroides johnsonii N1 N1 Parabacteroides merdae N1 N2 N3 FUSOBACTERIA Fusobacteriaceae Fusobacterium sp. N1 N1 N1 FIRMICUTES Acidaminococcaceae Acidaminococcus intestine N1 Bacillaceae Bacillus amyloliquefaciens N1 N1 N1 Bacillus cereus O5 Bacillus licheniformis O1 Bacillus pumilus O1 Bacillus sonorensis O1 Bacillus subtilis O1 O1 Clostridiaceae Clostridium perfringens N1 N1 Clostridium tertium O1 O1 Enterococcaceae Enterococcus avium O1 O1 Enterococcus casseliﬂavus O1 O1 O1 Enterococcus durans d O2 Enterococcus faecalis d O2 O2 Enterococcus faecium d O9 N1 N1 O3 N2 Erysipelotrichaceae Coprobacillus cateniformis NC Catenibacterium mitsuokai N1 Erysipelatoclostridium ramosum c N2 Lachnospiraceae Lachnoclostridium scindens N1 N1 Lachnoclostridium symbiosum c N1 N1 N1 Lactobacillaceae Lactobacillus buchneri NC Lactobacillus delbrueckii c N1 Lactobacillus paracasei NC NC Lactobacillus rhamnosus O1/N1 O1/N1 Lactobacillus ruminis NC Lactobacillus sakei NC Pediococcus acidilactici c O1 O1 Oscillospiraceae Oscillibacter sp. N1 N1 N1 N1 Int. J. Mol. Sci. 2020, 21, 1986 5 of 19

Table 1. Cont.

CKD 1 (n = 6) CKD 5 (n = 6) Bacterial Species Neg pC Phe Ind IAA Neg pC Phe Ind IAA Ruminococcaceae Flavonifractor plautii NC Staphylococcaceae Staphylococcus capitis N1 N1 Staphylococcus epidermidis N1 N1 Staphylococcus haemolyticus O2 O2 O2 Staphylococcus hominis NC Streptococcaceae Lactococcus lactis NC Streptococcus agalactiae O1 Streptococcus anginosus NC Streptococcus gordonii O1 O1 Streptococcus lutetiensis O1/N2 Streptococcus oralis/mitis a O3 Streptococcus parasanguinis NC Streptococcus sanguinis/ N1 N1 australis b Streptococcus vestibularis/ O1 O1 O3 salivarius a Veillonellaceae Megasphaera elsdenii NC NC Veillonella atypica NC Veillonella dispar NC PROTEOBACTERIA Caulobacteraceae Brevundimonas diminuta O1 Desulfovibrionaceae Bilophila wadsworthia N1 N1 Enterobacteriaceae Citrobacter freundii O1 O1 Enterobacter cloacae/kobei/ludwigii O6/N1 /xiangfangensis/hormaechei a Escherichia coli * O7/N1 O7/N1 O15/N4 O15/N4 Klebsiella variicola c O1 O1 Pseudomonadaceae Pseudomonas aeruginosa O1 O1 Pseudomonas stutzeri O1 Sutterellaceae Sutterella wadsworthensis NC Xanthomonodaceae Stenotrophomonas maltophilia NC NO IDENTIFICATION◦ MB 208_49 N1 N1 N1 N1 MB 210_6 N1 MB 210_38 N1 N1 MB 242_16 N1 MB 256_36 N1 MB 256_39 N1 N1 MB 256_61 N1 N1 N1 MB 263_32 N1 MB 275_15 N1 N1 MB 275_16 N1 MB 300_21 N1 N1 MB 314_01 O1 O1 Bacterial taxonomy according to Uniprot [50]; a: species not distinguishable with MALDI-TOF; b: species not distinguishable with 16S rRNA gene sequencing; c: species with generation of unknown peak 26 on HPLC; d: species with generation of an unknown peak 9 on HPLC; ◦: no identiﬁcation after MALDI-TOF and/or 16S rRNA gene sequencing; *: not distinguishable with Shigella spp. with MALDI-TOF; Neg.: No in vitro PBUT precursor generation capacity; pC: p-cresol; Phe: phenol; Ind: indole; IAA: indole-3-acetic acid; O (oxygen): in vitro PBUT precursor generation capacity under aerobic conditions (37 ◦C for 2 days); N (nitrogen): in vitro PBUT precursor x x generation capacity under anaerobic conditions (37 ◦C for 6 days); x (in O /N ): Number of isolates tested for uremic toxin precursor generation; NC: could not be further cultured in broth to check the generation capacity of PBUT precursors.

2.3. Quantification of Fecal Bacterial Species in Different Stages Of CKD Quantitative PCR (qPCR) analysis was assessed in different stages of CKD and in controls for 11 bacterial taxa, and the total number of 16S rRNA gene copies with a universal bacterial qPCR Int. J. Mol. Sci. 2020, 21, 1986 6 of 19

(Figure1). Prior to statistical analysis, one CKD 1 sample was excluded due to the absence of qPCR signal for each of the performed assays (LOD > Concentration). The abundance of Bacteroides spp., Bifidobacterium spp., Faecalibacterium prausnitzii and Akkermansia muciniphila, as well as the total number of bacterial 16S rRNA gene copies was comparable among the different stages of CKD and the control group. In fecal samples from hemodialysis (HD) patients, a higher abundance of Streptococcus spp. and Enterobacteriaceae was found compared to controls and early stages of CKD, and for Escherichia coli compared to controls. Moreover, compared to HD patients, the abundance of Lactobacillus spp. decreased in CKD stage 1–5 patients, and the abundance of Clostridioides difficile in all other CKD stages and controls. Decreased levels of Roseburia spp. were observed in CKD 5 compared to controls, CKD 1, HD and peritoneal dialysis (PD) patients. In addition, a lower abundance of Streptococcus spp. was found in CKD 3 and CKD 4 compared to controls, while the opposite was found for E. coli, i.e., an increase in CKD 3 compared to control. Finally, decrease in kidney function (ranging from control to CKD 5) positively correlated with the abundance of Enterobacteriaceae (rs = 0.210), and E. coli (rs = 0.286), while an inverse correlation was found with Streptococcus spp. (rs = 0.255), Butyricicoccus − spp. (rs = 0.326), F. prausnitzii (rs = 0.250), Roseburia spp. (rs = 0.342) and Bifidobacterium spp. Int. J.− Mol. Sci. 2020, 21, x − − 7 of 20 (rs = 0.303) (Figure1; all p < 0.05). Similar results were obtained when correlating the abundance − of the bacterial(rs = −0.342) taxa and withBifidobacterium estimated spp. glomerular (rs = −0.303) (Figure filtration 1; all rate p < 0.05). (eGFR), Similar except results for wereStreptococcus obtained spp. (Table S2).when correlating the abundance of the bacterial taxa with estimated glomerular filtration rate (eGFR), except for Streptococcus spp. (Table S2).

Figure 1.FigureBacterial 1. Bacterial gene copies gene copies per gram per gram feces perfeces chronic per chronic kidney kidney disease disease (CKD) (CKD) stage: stage: (a )(a Total) Total bacterial 16S rRNA;bacterial (b) Clostridioides 16S rRNA; ( dib)ﬃ cileClostridioides;(c) Streptococcus difficile; (cspp.;) Streptococcus (d) Bacteroides spp.; spp.;(d) Bacteroides (e) Enterobacteriaceae spp.; (e) ;(f) EscherichiaEnterobacteriaceae coli;(g) Butyricicoccus; (f) Escherichiaspp.; coli; ( h(g)) FaecalibacteriumButyricicoccus spp.; prausnitzii(h) Faecalibacterium;(i) Roseburia prausnitziispp.;; (i) Roseburia (j) Akkermansia spp.; (j) Akkermansia muciniphila; (k) Bifidobacterium spp.; (l) Lactobacillus spp.. *: p ≤ 0.05; **: p ≤ 0.001; muciniphila;(k) Biﬁdobacterium spp.; (l) Lactobacillus spp. *: p 0.05; **: p 0.001; rs: Spearman’s rank rs: Spearman’s rank test; gray square: correlation between respective≤ bacteria≤ versus CKD stages 1–5 test; gray square: correlation between respective bacteria versus CKD stages 1–5 and control. and control.

Comparison of the total bacterial number and the abundance of the 11 PBUT precursor- generating bacterial taxa in the control, CKD (CKD stages 1 to 5) and dialysis groups (HD and PD patients), revealed no significant differences for the total bacterial 16S rRNA gene copies, the abundance of Bacteroides spp., Bifidobacterium spp., Butyricicoccus spp., F. prausnitzii, and Roseburia spp. (Figure 2). However, a higher abundance was found in the dialysis group compared to CKD for A. muciniphila, C. difficile, Enterobacteriaceae, Lactobacillus spp., and Streptococcus spp., and compared to controls for C. difficile, Enterobacteriaceae, and E. coli. In addition, in CKD, the abundance of Streptocococcus spp. decreased and of E. coli increased compared to the control group (Figure 2).

Int. J. Mol. Sci. 2020, 21, 1986 7 of 19

Comparison of the total bacterial number and the abundance of the 11 PBUT precursor-generating bacterial taxa in the control, CKD (CKD stages 1 to 5) and dialysis groups (HD and PD patients), revealed no significant differences for the total bacterial 16S rRNA gene copies, the abundance of Bacteroides spp., Bifidobacterium spp., Butyricicoccus spp., F. prausnitzii, and Roseburia spp. (Figure2). However, a higher abundance was found in the dialysis group compared to CKD for A. muciniphila, C. difficile, Enterobacteriaceae, Lactobacillus spp., and Streptococcus spp., and compared to controls for C. difficile, Enterobacteriaceae, and E. coli. In addition, in CKD, the abundance of Streptocococcus spp. decreased and of E. coli increased compared to the control group (Figure2). Int. J. Mol. Sci. 2020, 21, x 8 of 20

FigureFigure 2. Bacterial 2. Bacterial gene gene copies copies per gramper gram feces feces forthe forcontrol, the control, CKD CKD and dialysisand dialysis group: group: (a) Total(a) Total bacterial 16S rRNA;bacterial (b )16SClostridioides rRNA; (b di) ffiClostridioidescile;(c) Streptococcus difficile; (cspp.;) Streptococcus (d) Bacteroides spp.; spp.;(d) Bacteroides (e) Enterobacteriaceae spp.; (e) ;(f) EscherichiaEnterobacteriaceae coli;(g) Butyricicoccus; (f) Escherichiaspp.; coli; ( (gh)) ButyricicoccusFaecalibacterium spp.; prausnitzii(h) Faecalibacterium;(i) Roseburia prausnitziispp.;; (i ()j Roseburia) Akkermansia muciniphilaspp.; (j;() Akkermansiak) Bifidobacterium muciniphilaspp.;; (k ()l )BifidobacteriumLactobacillus spp.spp.; ( *:l) pLactobacillus0.05; ** pspp..0.001. *: p ≤ 0.05; **p ≤ 0.001. ≤ ≤ 3. Discussion3. Discussion The gutThe microbiotagut microbiota is responsible is responsible for for the the generation generation of of PBUTs PBUTs [ 10[10,12,51],,12,51], which accumulate accumulate in in the bloodthe circulation blood circulation of patients of patients with CKDwith CKD [1–3 ].[1–3]. At the At the moment, moment, no no eff effectiveective therapies therapies exist to to reduce reduce the plasmathe levels plasma of levels these of PBUTs. these ThisPBUTs. is theThis first is the study first to study isolate to andisolate identify and identify PBUT precursor-generatingPBUT precursor- generating bacteria from fecal samples from patients with CKD stage 1 and 5 patients. These results bacteria from fecal samples from patients with CKD stage 1 and 5 patients. These results give us more give us more insight regarding which bacteria are responsible for the metabolization of aromatic insightamino regarding acids into which phenolic bacteria (i.e., p are-cresol responsible and phenol) for and the indolic metabolization (i.e., indole of and aromatic IAA) compounds. amino acids In into phenolicaddition, (i.e., p11-cresol PBUT and precursor-generating phenol) and indolic bacterial (i.e., indole taxa andand IAA)the total compounds. number of In addition,bacteria were 11 PBUT quantified, using qPCR, in patients with different stages of CKD and healthy controls. In total, 148 different bacterial species were isolated, of which 92 could be classified as PBUT precursor-generating bacteria. Moreover, to our knowledge, 51 of the isolated bacterial species were not previously described as generators of phenolic or indolic compounds. Fecal proteolytic bacterial species predominantly belong to the genera Bacteroides and Propionibacterium, and although with fewer representatives, also to the genera Bacillus, Clostridium, Staphylococcus and Streptococcus [52]. This is in line with the findings of the present study, in which the Bacteroidaceae, Clostridiaceae, Lachnospiraceae and Staphylococcaceae were the predominant generators of phenolic compounds in

Int. J. Mol. Sci. 2020, 21, 1986 8 of 19 precursor-generating bacterial taxa and the total number of bacteria were quantified, using qPCR, in patients with different stages of CKD and healthy controls. In total, 148 different bacterial species were isolated, of which 92 could be classified as PBUT precursor-generating bacteria. Moreover, to our knowledge, 51 of the isolated bacterial species were not previously described as generators of phenolic or indolic compounds. Fecal proteolytic bacterial species predominantly belong to the genera Bacteroides and Propionibacterium, and although with fewer representatives, also to the genera Bacillus, Clostridium, Staphylococcus and Streptococcus [52]. This is in line with the findings of the present study, in which the Bacteroidaceae, Clostridiaceae, Lachnospiraceae and Staphylococcaceae were the predominant generators of phenolic compounds in addition to IAA. In the current study, these metabolites were also generated by bacterial species belonging to the Actinomycetaceae, Tannerellacae, and Enterococcaceae, of which the latter family also was classified as a phenolic compound-generating bacterial taxon [9]. Most fecal species with the capacity to generate phenolic compounds belong to the genus Bacteroides, although species-to-species differences have been reported [37,40,53]. These differences in the capacity to generate phenolic compounds by Bacteroides spp. can be explained by the different compositions of the culture media used and thus by a different substrate availability, which determines the metabolization pathway of gut bacteria (carbohydrate or amino acid metabolization). This has already been reported for Bifidobacterium spp., which can switch to the metabolization of amino acids in the absence of carbohydrates [36]. Because of the presence of the tyrosine deaminase gene, Clostridiaceae and Enterobacteriaceae also have the property of converting tyrosine into p-cresol, which were increased in ESKD patients [27]. However, in the present study, the generation of p-cresol was only observed in bacterial species belonging to the Clostridiaceae. Moreover, in the present study, bacterial species generating solely IAA belong to the Bacillaceae, Bifidobacteriaceae, Brevibacteriaceae, Coriobacteriaceae, Corynebacteriaceae, Eggerthellaceae, Microbacteriaceae and Micrococcaceae, while the other bacterial species mostly also produce indole and/or phenolic compounds. Conversion of tryptophan into indole is performed by tryptophanase, an enzyme known to be expressed by (at least) 27 gut-associated genera [54]. Tryptophanase activity is also present in Clostridiaceae, Enterobacteriaceae and Verrucomicrobia, which all have been reported to be elevated in ESKD patients [27]. Interestingly, in this study, most of the Lactobacillus spp. generate none of the measured indolic and phenolic compounds, but are potent generators of lactic acid and phenyl lactic acid [55,56]. In healthy conditions, most abundant Lactobacillus spp. in the colon include L. casei, L. delbrueckii, L. plantarum, L. rhamnosus, and L. ruminus [57], with the exception of L. plantarum, all of these were also isolated from the CKD fecal samples in this study. Based on the bacterial generation of phenolic and indolic compounds in vitro and the associations between bacterial taxa with high pCS or high IxS serum levels in HD patients [58], 11 bacterial taxa were selected to explore the abundance of these PBUT precursor-generators in different stages of CKD. Altered gut microbial composition in CKD was observed by Vaziri et al., revealing a qualitative difference of 190 operational taxonomic units between HD patients and controls [21]. A change in the gut microbial composition is confirmed in our CKD cohort with changes in the abundance of Butyricicoccus spp., C. difficile, Enterobacteriaceae, E. coli, Lactobacillus spp., Roseburia spp. and Streptococcus spp. in the different stages of CKD and control. Moreover, a decline in kidney function was positively associated with Enterobacteriaceae and E. coli, and negatively associated with Bifidobacterium spp., Butyricicoccus spp., F. prausnitzii, Roseburia spp. and Streptococcus spp.. However, the total number of bacterial 16S rRNA gene copies was similar among all CKD stages and controls, in line with two other studies comparing (non-)dialyzed ESKD patients with controls [29,59]. In a cultured-based approach to estimate the total bacterial number, no significant differences were observed between HD and controls, either [30], while in another culture study, a lower total number of bacteria were observed in CKD and HD patients [23]. For Bacteroides spp., we found comparable amounts over the different CKD stages and controls, which was also reported for B. fragilis in PD patients compared to controls [28]. Bacteroides is one of the most abundant bacterial genera in the gut of healthy adults and patients with CKD [60]. Enrichment of aerobic bacteria in the colon of patients with CKD has been frequently Int. J. Mol. Sci. 2020, 21, 1986 9 of 19 reported [23,29,30,61,62], and was also confirmed in the present study (i.e., Enterobacteriaceae and E. coli). However, this was not the case for all studies assessing the gut microbial composition in CKD. A qPCR-based study found no differences in E. coli levels in PD patients and controls [63], while another study found decreased E. coli levels in Chinese ESKD patients [29]. In addition, in this study, the abundance of Streptococcus spp. decreased with impaired kidney function, and increased again under dialysis therapy. Similar results were found in the study of Stadlbauer et al., with higher Streptococcus spp. levels in HD [62] and type 2 diabetes mellitus CKD patients [64] compared to controls. Elevated levels of Streptococcus spp. have been associated with a range of human infections [65]. The previously observed decrease of anaerobic bacteria in CKD [23,29] was also confirmed in the present study with the decrease of Bifidobacterium spp. and the inverse correlation of Bifidobacterium spp., Butyricicoccus spp., F. prausnitzii and Roseburia spp. with impaired kidney function. Bifidobacteria and lactobacilli are commonly used probiotics, aiming at health-promoting effects to the host and commensal gut microbiol homeostasis [66]. In our CKD cohort, the abundance of Lactobacillus spp. was higher in HD patients compared to patients with CKD stages 1 to 5. In a previous sequence-based comparison of the gut microbiota between patients with CKD stages 1 to 5 and non-CKD controls; however, higher Lactobacillus spp. levels in CKD were found [60]. Another health-promoting bacterial species is Akkermansia muciniphila, an essential propionate-generating and mucus-degrading species that is beneficial through the production of energy from mucus degradation and as such supporting the growth of butyrate-producing bacteria [60,67–69]. A sequencing study comparing CKD patients with controls reported a strong negative correlation with CKD and a reduction of the genus Akkermansia, of which the species A. muciniphila is the only human representative [60]. These findings are in contrast to the present study with only a lower abundance of A. muciniphila in the CKD group versus the dialysis group, and no inverse correlation with loss of kidney function. Other short chain fatty acid (SCFA)-generating bacteria include Butyricicoccus spp., Faecalibacterium prausnitzii and Roseburia spp., which predominantly colonize the colonic mucus layer and particularly generate butyrate. Butyrate is beneficial for the health because it is an energy source for colonocytes and it promotes cell differentiation, suppresses colonic inflammatory response, decreases luminal pH and improves tight junction assembly [59,69–71]. Our results show that the three aforementioned butyrate-producing taxa were inversely associated with impaired kidney function. This is consistent with previous findings observing a decrease of butyrate-generating bacteria in ESKD patients compared to controls [29,61,62,72] and depletion of microbial families possessing butyrate-forming enzymes in ESKD patients [27]. However, in the study of Terpstra et al., the abundance of F. prausnitzii and Roseburia spp. was comparable in ESKD patients and controls [59]. Finally, the pathogenic bacterium C. difficile, which is related to severe antibiotic-associated diarrhea [73,74] and a well-known producer of p-cresol [39,40], was elevated in the HD group of the present study. This increased abundance could be explained by elevated antibiotic intake due to a higher infection rate in ESKD patients [74]. p-Cresol and indole originate from two independent metabolization pathways [9,17], which are associated with a completely different gut microbial composition [58]. p-Cresol exerts various toxic effects to biological functions [75–82]. This is in contrast to indole, which acts as an intercellular signal molecule and contributes to the immune cell homeostasis and the host-microbe homeostasis at the mucosal surface by promoting gene expression of gut epithelial cell junctions [17,35,83]. Of interest, only a small fraction of tryptophan (~5%) in the gut is converted into indole, while the other fraction is used in the kynurenine (~95%) and serotonin (~1-2%) pathway [17,54]. The beneficial properties of indole imply that it is less desirable to reduce the generation of indole compared to the generation of p-cresol, and in this context, the differentiated metabolization pathways of p-cresol and indole could be considered as an advantage rather than a disadvantage. Pro-, pre- and synbiotics could play an important role in minimizing the generation of only p-cresol and thus also of the plasma levels of pCS, which is supported by numerous intervention studies in patients with CKD, showing only a reduction of plasma pCS or IxS levels, but not of both toxins [30,41–46]. Of note, a decrease of aerobic bacteria, generating mostly indolic compounds, could be beneficial to the gut microbial homeostasis considering Int. J. Mol. Sci. 2020, 21, 1986 10 of 19 the present aerobic overgrowth in CKD. These elevated levels of aerobes, shown to inhibit anaerobic bacterial growth [23,30], could also negatively interfere with the activity of SCFA-generating bacteria. As shown in the present study, phenolic compounds were mostly generated by bacterial species from the phyla Bacteroidetes (i.e., Actinomycetaceae, Bacteroidaceae, Odoribacter splanchnicus, Prevotella copri and Tannerellaceae) and Firmicutes (i.e., Clostridiaceae, Enterococcaceae, Lachnospiraceae, Oscillibacter sp., Staphylococcaceae and Streptococcaceae). Quantification of Bacteroides spp., C. difficile and Streptococcus spp. with qPCR revealed elevated levels of C. difficile and Streptococcus spp. in HD patients which marks both taxa as potential targets to reduce p-cresol generation in patients with CKD. Bacteroides spp. did not change in abundance in CKD versus controls, but could also be a target as these have the capacity to generate phenolic compounds. To our knowledge, no interventions have so far been assessed to reduce one of these bacterial taxa in patients with CKD or in healthy controls. Because our results indicate that both Bifidobacterium spp. and Lactobacillus spp. are reduced with impaired kidney function and although they are known to generate only IAA, these taxa may be potential probiotics. In CKD, numerous studies have already addressed the impact of these bacterial species on the reduction of plasma pCS and IxS levels and their gut-derived precursor metabolites [30,41–47,84,85]. Fecal p-cresol, fecal indole and plasma IxS could be reduced by a combination of antibiotic-resistant lactic acid-generating bacteria (i.e., B. infantis, L. acidophilus and Enterococcus faecalis)[30]. In addition, a synbiotic treatment, consisting of B. breve Yakult, L. casei Shirota and oligofructose-enriched inulin (OF-IN), reduced urinary p-cresol, a proxy for colonic p-cresol generation, and promoted the growth of bifidobacteria in healthy volunteers [84]. This is in line with the SYNERGY trial, showing elevated levels of bifidobacteria in CKD stage 4 and stage 5 patients after intake of a synbiotic containing galactooligosaccharides (GOS), OF, Bifidobacterium spp., Lactobacillus spp. and Streptococcus spp. [41]. Moreover, in the same study, excluding the patients receiving antibiotic treatments, the relative abundance levels of F. prausnitzii increased and serum pCS levels decreased in the patients with CKD [41]. Another study assessed the stimulation of saccharolytic fermentation and the reduction of proteolytic fermentation attributed by the combination of L. casei Shirota and OF-IN [47]. Elevation of SCFA-generating bacteria through high total dietary fiber intake may contribute to the reduction of microinflammation present in CKD, and could hamper the progression of CKD [72,86]. In HD patients, plasma pCS levels could be reduced by consumption of the prebiotic OF-IN [42] and intake of the synbiotic containing L. casei Shirota, B. breve, GOS, lactose and monosaccharide [44]. Moreover, the synbiotic Probinul-neutro® (CadiGroup, Rome, Italy) reduced plasma pCS levels in CKD stage 3 and stage 4 [45], while levels of IxS could be reduced by resistant starch [46] and by B. longum [43] in HD patients. These studies indicate promising therapeutic interventions to reduce fecal p-cresol and plasma pCS and IxS levels in CKD. Moreover, even in earlier CKD stages, already associated with a change in the gut microbial composition [87], which might delay the progression of the disease. In a recent paper by our group [34], regarding the same study population, the protein intake was estimated by calculating the ratio of urinary urea over urinary creatinine, a proxy for protein intake. This ratio was not significantly different among the different stages of CKD nor in comparison to the control group and therefore we cannot attribute the observed changes in gut bacterial composition to differences in protein intake in this study population. A possible shortcoming of this study was the assessment of the gut microbial community by only qPCR, depending on a selection of gut bacterial taxa, and not of the entire microbial community by means of high-throughput molecular techniques. However, our qPCR assays indicate an alteration in the gut microbial composition over the different stages of CKD and controls for nine bacterial taxa, of which some are at species level. However, in a previous microbiome study by our research group, the gut microbial composition of the HD patients from the current CKD cohort was analyzed with phylogenetic community profiling and revealed a similar microbial profile, based on genera and higher taxonomic taxa, was observed with that of controls [58]. This was in line with another sequencing study comparing ESKD patients with controls [29], but not in a study comparing controls with non-dialyzed Int. J. Mol. Sci. 2020, 21, 1986 11 of 19 and dialyzed CKD patients [61,88]. The sequencing results of the CKD group are in preparation for another paper, in view of the extent of the data. In summary, isolation and quantification of PBUT precursor-generating bacteria from fecal samples of patients with CKD (stages 1 and 5) showed that phenolic compounds are mostly generated by anaerobic bacteria whereas indolic compounds are produced by both anaerobes and aerobes. The present study is one of the first investigating PBUT precursor-generating bacterial concentrations with qPCR in all stages of CKD, and indicated that kidney function decline is accompanied by a decrease in the abundance of SCFA-generating bacteria, Bifidobacterium spp. and Streptococcus spp., and an increase of aerobic bacteria, i.e., Enterobacteriaceae and E. coli. Our findings might trigger the development of potential interventions to minimize the generation of especially p-cresol by the gut microbiota and subsequently to reduce plasma pCS levels in patients with CKD. Moreover, in the future, these bacterial species with the capacity to generate PBUT precursor metabolites need to be assessed in in vitro models and in vivo studies to elucidate their pathophysiological role, e.g., their effect on the intestinal barrier function and inflammation, in CKD.

4. Materials and Methods

4.1. Study Population and Sample Collection The study population, from which a fecal sample was collected, has been previously used and described, including the clinical parameters, by the authors [34]. Briefly, in total, 14 healthy controls, 111 non-dialyzed CKD patients (stages 1–5), 16 HD patients, and 11 PD patients were included. Exclusion criteria were active infection (C-reactive protein, CRP > 20mg/L), immunosuppressive therapy, obesity (Body Mass Index > 35), inflammatory bowel disease, active malignancy, cardiovascular event in the past three months, pregnancy, transplantation, use of non-steroidal anti-inflammatory drugs within the past month, vascular access problems for HD patients, and age < 18 years. Before inclusion, all patients and healthy controls gave written informed consent. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Medial Ethics Committee of the Ghent University Hospital (Ref 2010/033, B67020107926 and Ref 2012/603, B670201214999).

4.2. Culture Media Prior to the isolation of PBUT precursor-generating bacteria, appropriate culture media were selected. For this purpose, a total of 28 different culture broths (Figure S1) were compared with respect to their ability to support growth of a test panel, composed of bacterial species known to generate p-cresol and/or indole. AAA (total concentration of 2.5 mM l-tyrosine, 2.5 mM l-phenylalanine and 2.5 mM l-tryptophan, Sigma Aldrich, Saint Louis, MO, USA) were added to stimulate the growth of proteolytic (fermentation) bacteria. Moreover, to mimic the natural gut environment of the fecal bacteria, a sterile FS was prepared and added to some of the media. For this, whole fresh fecal samples (n = 13; 29 g to 173 g) from healthy controls were mixed with a same fecal weight volume of PBS (Phosphate buffered saline, Lonza, Basel, Switzerland) using a vortex. The fecal suspension was centrifuged at 10,000 g for 30 min and the supernatant was centrifuged again at 30,000 g for × × 15 min. The supernatant was stored at 80 C after vacuum filtration with a 0.22 µm filter (Steritop − ◦ threaded bottle top filter, Merck Chemicals, Brussels, Belgium) to remove viable bacteria. The different media tested are summarized in Table S3. The bacterial test panel included Bacteroides fragilis (LMG 10263T), B. thetaiotaomicron (LMG 11262), Bifidobacterium adolescentis (LMG 10502T), B. bifidum (LMG 11041T), B. longum (LMG 13197T), Clostridioides difficile (LMG 21717T); Clostridium sporogenes (LMG 8421T t1), Lactobacillus acidophilus (CHCC 3777), L. paracasei (LAB109cas), Paraclostridium bifermentans (LMG 3029), Escherichia coli (LMG 2092T) and Staphylococcus epidermidis (U9108901). Growth of the bacterial test panel in the broth media was determined spectrophotometrically at 600 nm (Glomax® Explorer Multimode Microplate Reader, Promega, Madison, Wisconsin) and growth was also confirmed inoculating the broth onto SCH agar plates. Int. J. Mol. Sci. 2020, 21, 1986 12 of 19

Furthermore, to select the culture media that enabled maximal recovery of different fecal bacteria, three fecal samples, from three controls, were cultured on five selected media, i.e., the only ones out of 28 combinations that supported growth of all species of the test panel. For this purpose, 1 g of each of the samples was homogenized and 0.5 g of the homogenate was used to make a tenfold serial dilution, using maximum recovery diluent solution (MRD, Sigma Aldrich). Fecal dilutions of 10-2 to 10-4 were -4 -6 used to inoculate agar plates for aerobic incubation (2 days at 37 ◦C), and fecal dilutions of 10 to 10 for anaerobic incubation (7 days at 37 ◦C) (Figure S2). The colony forming units (CFU) on each agar plate were counted and identified.

4.3. Isolation of Protein-Bound Uremic Toxin Precursor-Generating Bacteria from CKD Fecal Samples Fecal samples were randomly selected from CKD 1 (n = 6) and CKD 5 (n = 6) patients and used to culture in vitro PBUT precursor-generating bacteria. First, a tenfold serial dilution of each 2 4 fecal sample was prepared, with MRD as diluent solution. Fecal dilutions of 10− to 10− were used 4 6 for aerobic incubation (2 days at 37 ◦C), and fecal dilutions of 10− to 10− for anaerobic incubation (6 days at 37 ◦C) were inoculated on YCFAG ∆0 +AAA +FS, SCH and 1203a media (CM0957 Anaerobe Basal Broth, Oxoid, Thermo Fisher Scientiﬁc, Merelbeke, Belgium), of which the last medium was recommended by the Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSMZ) to culture Akkermansia muciniphila.

4.4. Identification of Fecal Protein-Bound Uremic Toxin Precursor-Generating Bacteria Direct identification of bacterial colonies was performed with a matrix assisted laser desorption/ionization time-of-flight analyzer (MALDI-TOF, Bruker, Mannheim, Germany). For MALDI-TOF identification, the bacterial colonies were spotted on a MALDI-TOF plate and 1 µL of Bruker HCCA matrix solution (For 250 mL: 2.5 0.3 mg α-Cyano-4-hydroxycinnamic acid, 125 mL ± acetonitrile, 118.75 mL water, and 6.25 mL trifluoroacetic acid) was added. In case of no identification with MALDI-TOF, 16S rRNA gene sequencing was performed by GATC (Eurofins Genomics, Ebersberg, Germany).

4.5. Quantification of p-Cresol, Phenol, Indole and Indole-3-Acetic Acid in Broth Media The concentrations of p-cresol (108 Da) and indole (117 Da) in the broth medium were measured with reversed-phase HPLC. Chromatographic HPLC separation was performed at 26 ◦C on a reversed-phase XBridge C8 column (3.5 µm, 150 4.6 mm, Waters, Zellik, Belgium) with an Ultrasphere × 5 ODS Guard column (5 µm, 45 4.6 mm, Hichrom, Reading, UK). The mobile phase consisted of × 50 mM ammonium formate buffer (mobile phase A, pH 3.0) and methanol (mobile phase B). The chromatographic separation consisted of a linear gradient at a flow of 1 mL/min, starting with 100% A during the first 3 min, followed by a change into 100% B during the next 36 min. The latter composition was held for 3 min and followed by a re-equilibration step. Fecal suspensions were filtered before chromatography through an Amicon Ultra 0.5 mL filter (molecular weight cut-off 30 kDa, Millipore Merck, Darmstadt, Germany). p-Cresol (λex: 278 nm, λem: 304 nm), and indole (λex: 275 nm, λem: 334 nm) were detected by a Waters 2475 fluorescence detector. Concentrations of phenol (94 Da) and IAA (175 Da) were measured with reversed-phase UPLC analysis. For the quantification of the total concentration of IAA, samples were deproteinized by heat denaturation, followed by a filtration step through an Amicon Ultra 0.5 mL filter prior to reversed-phase UPLC analysis [89]. Chromatographic UPLC separation was performed at 26 ◦C on a Waters Acquity UPLC BEH C18 Van Guard column (1.7 µm, 100 x 2.1 mm). The mobile phase consisted of 50 mM ammonium formate buffer (mobile phase A, pH 3.0) and methanol (mobile phase B). A linear gradient elution was used to separate the compounds at a flow rate of 0.3 mL/min, and started at 98% A, followed by a composition change to 90% A in 7 min. In the next 9 min, the mobile phase changed to 100% B and was held for 3 min. Finally, a re-equilibration step was performed. Phenol (λex: 275 nm, Int. J. Mol. Sci. 2020, 21, 1986 13 of 19

λem: 350 nm) and IAA (λex: 280 nm, λem: 350 nm) were detected by an Agilent G1316C ﬂuorescence detector (Agilent, Diegem, Belgium).

4.6. Quantification of Bacterial Species in Fecal Samples To perform qPCR assays, bacterial DNA was extracted from fecal samples using the PowerMicrobiome RNA isolation kit (Qiagen, Hilden, Germany) with the following modifications: the DNase steps (steps 12 to 16) were not performed, in order to extract DNA next to RNA. Furthermore, an additional heating step of 95 ◦C for 10 min after step 4 was added, to increase the DNA yield [90,91]. In addition to the total bacterial number also the abundance of 11 bacterial taxa in different stages of CKD and controls was assessed. Of these, Bacteroides spp., Bifidobacterium spp., C. difficile, Enterobacteriaceae, E. coli, F. prausnitzii, Roseburia spp., and Streptococcus spp. were selected based on their PBUT precursor-generating capacity, observed in the present study and in in vitro studies [9]. Moreover, A. muciniphila, Bacteroides spp., F. prausnitzii, Lactobacillus spp., Roseburia spp., and Streptococcus spp. belonged to the top 10 bacterial taxa with the largest differences in proportions between HD patients with high pCS/low IxS versus low pCS/high IxS serum levels, shown in a previous study of our group [58], for which reason these were included here. Finally, the abundance of a well-known SCFA-generator Butyricicoccus spp. [71] was also assessed. To quantify the bacterial taxa from fecal samples, family-, genus- and species-specific qPCRs were developed and performed on the basis of previously described formats [71,91–103]. Primers and probes used are summarized in Table S4. The specificity of primers and probes was evaluated in silico using Nucleotide BLAST (basic local alignment search tool). Primer sequences and the amplicon sequence were further evaluated with the programs “OligoAnalyzer”, “mfold” and “DINAMelt” for homo- and hetero-dimers, sequence length, GC percentage, and melting temperature. All primers and the probe were purchased from Kaneka Eurogentec (Seraing, Belgium). To validate the specificity of the amplification of the primers and probes, qPCR amplification was performed using template DNA from different bacterial species (Table S5), with the LightCycler480 (LC480, Roche Life Science, Vilvoorde, Belgium). Table S4 summarizes the qPCR mixes and thermal cycling conditions for each qPCR. To verify the specificity of the qPCRs without the usage of a probe, melting curve analysis was performed. Of every qPCR, the limit of quantification (LOQ) and LOD was determined, as described by Forootan et al. [104] (Table S4). For all qPCR reactions, every sample was amplified in duplicate, standard dilution series were used as positive controls, and qPCR mixes without DNA were included as negative controls. Standard tenfold dilution series were prepared from DNA extracts indicated in Table S5.

4.7. Statistical Analysis All statistical analyses were performed by IBM SPSS Statistics for Windows, version 26 (IBM, Armonk, NY, USA) and graphs were made with R (2019, version 3.6.0). Prior to statistical analysis of the qPCR concentration results of the bacterial species, the data were log10 transformed. To determine the normality of the data, a Shapiro-Wilk test was performed. Depending on the distribution, the data are presented as mean standard deviation or median (25th–75th percentile). To compare the ± characteristics and the bacterial concentrations between the different stages of CKD (control, CKD stage 1–5, HD and PD), an ANOVA or a Kruskal-Wallis test, both with a Benjamini-Hochberg correction for multiple testing, was performed according the data distribution. Correlations between the abundance of the bacterial taxa and different study groups (control and CKD 1-5) or eGFR, were performed with the Spearman’s rank test (rs). Differences were considered as statistically significant when the p-value was below 0.05.

Supplementary Materials: Supplementary materials can be found at http://www.mdpi.com/1422-0067/21/6/1986/ s1. Figure S1: Ability of the 12 bacterial species of the test panel to grow on 28 different media, Figure S2: Isolation and identification of protein-bound uremic toxin (PBUT) precursor-generating bacteria; Table S1: Isolated organisms from three fecal samples on five different media; Table S2: Correlation between the abundance of the Int. J. Mol. Sci. 2020, 21, 1986 14 of 19 bacterial taxa and estimated glomerular filtration rate (eGFR); Table S3: Media composition (for 1L), Table S4: Primers, probes and thermal cycling conditions for all performed qPCRs, Table S5: qPCR specificity. Author Contributions: Conceptualization, T.G., G.G. and M.V.; methodology, T.G., G.G. and M.V.; validation, T.G.; formal analysis, T.G.; investigation, T.G.; writing—original draft preparation, T.G.; writing—review and editing, T.G., M.V., M.J., W.v.B., G.R.B.H., and G.G.; visualization, T.G.; supervision, G.G., M.V. and M.J. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by THE RESEARCH FOUNDATION FLANDERS (FWO), grant number G017815N. Acknowledgments: The authors thank Maria Van Landschoot and Tom Mertens for the H/UPLC analyses, and Aylin Uckuyulu for the assistance in the lab. The authors thank the attending nephrologists and the nurses for their effort in recruiting patients and for the collection of the samples, respectively. The authors thank the nephrology unit at the University Hospital Antwerp for the recruitment of peritoneal dialysis patients. The authors thank the patients for their participation in the study. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Abbreviations

AAA Aromatic amino acids CKD Chronic kidney disease eGFR Estimated glomerular ﬁltration rate ESKD End stage kidney disease FS Fecal suspension GOS Galactooligosaccharides HD Hemodialysis IAA Indole-3-acetic acid IN Enriched inulin IxS Indoxyl sulfate LOD Limit of detection LOQ Limit of quantiﬁcation OF Oligofructose PBUT Protein-bound uremic toxin pCS p-Cresyl sulfate pCG p-Cresyl glucuronide PD Peritoneal dialysis SCFA Short chain fatty acid SCH Schaedler medium YCFAG Yeast Casitone Fatty Acid Glucose medium

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